In the past, a typical application for copiers or scan-to-print image processing systems was to reproduce an input image as accurately as possible, i.e., render a copy. Thus, copies have been rendered as accurately as possible, flaws and all. However, as customers become more knowledgeable in their document reproduction requirements, they recognize that an exact copy is often not what they want. Instead, they would rather obtain the best possible document output. Until recently, image quality from the output of a copier or a scan-to-print system was directly related to the input document quality. One very common set of input documents includes photographs. Unfortunately, photography is an inexact science, particularly among amateurs, and original photographs are often poor. Alternately, technology, age or image degradation variations often result in pictures having an unsatisfactory and undesirable appearance. What is desired then, is a copy giving the best possible picture, and not a copy of the original.
Photography has long dealt with this issue. Analog filters and illumination variations can improve the appearance of pictures in the analog photographic process. Thus, for example, yellow filters enhance the appearance of white clouds against a blue sky in black and white images. Further, various electrophotographic devices, including digital copiers, can clean up and improve images by adjustment of threshold, filtering, or background suppression. Generally, these methods are manual methods which a user must select on an image by image basis. Unfortunately, the casual user is not skilled enough to perform these operations. The inability to perform image enhancement operations is exacerbated when additionally dealing with color controls.
Three possible choices are presented by the art in the area of image enhancement. In the first case, we can do nothing. Such a system is a stable system, in that it does no harm to an image. This is a common approach taken to reproduction. However, the output documents of such a system are sometimes not satisfactory to the ultimate customer.
In a second case of image enhancement, the image can always be processed. It turns out that an improvement can usually be made to an image if certain assumptions are made that are accurate for most cases. In an exceptionally large set of images, increasing contrast, sharpness, and/or color saturation, will improve the image. This model tends to produce better images, but the process is unstable, in that for multi-generation copying, increases in contrast, saturation, or sharpness are undesirable and ultimately lead to a severe image degradation. Further, the process may undesirably operate on those images which are good ones.
Accordingly, we arrive at our third case of image enhancement, a process of automated image enhancement which operates to vary images which are not perceived as good images, but does not operate on images which do not need to be improved.
Many improvements can be made to an image, including exposure adjustment (described in U.S. Pat. No. 5,414,538), color balance correction (described in U.S. Pat. No. 5,357,352 and U.S. Pat. No. 5,371,615) or contrast enhancement (described in previously filed U.S. patent application Ser. No. 08/133,231, filed Oct. 7, 1993). Generally, these processing methods operate by modifying a set of tone reproduction curves (TRCs). The output image is achieved by using TRC curves, operating either on the luminance channel of an image expressed in LC.sub.1 C.sub.2 coordinates, or preferably on each channel in a color density space description of the image in Red-Green-Blue (rgb) coordinates.
One particular improvement that can be made to an image is enhancement of contrast. Contrast refers to the perception of the dynamic range of the image, or the range of densities within the possible densities at which the image is defined. Empirically, preferred images are relatively high in contrast, i.e., the image makes use of the entire dynamic range that is possible. The dynamic range of an image can be empirically measured by performing a histogram on the image, which determines how many pixels within the image have a particular intensity within the range of possible intensities. Preferred images tended to be characterized by histograms indicating that the entire dynamic range of the image is used. Algorithms exist that modify an image in a way as to generate a histogram that covers the entire dynamic range. The most common algorithm is the histogram flattening/histogram equalization algorithm as described in R. C. Gonzales and B. A. Fittes, "Gray level transformation for interactive image enhancement," Proc. Second Conference on Remotely Manned Systems 1975; F. L. Hall, "Almost uniform distributions for computer image enhancement," IEEE Trans. Comput. C-23, 207-208, 1974; W. K. Pratt, Digital Image Processing, Wiley, New York, 1978; M. P. Ekstrom, Digital Image Processing Techniques, Academic Press, Orlando, 1984; and J. C. Russ, The Image Processing Handbook, CRC Press, Boca Raton, 1992. However, when a histogram is globally flat, undesirable image artifacts are noted in a large number of cases where the application was to produce a visually pleasing image. Histogram equalization techniques perform well in cases where the application requires the detection of features in an image, as in medical or remote sensing applications. Modifications to the histogram equalization techniques are known as adaptive histogram equalization (AHE) as in S. M. Pizer et al., "Adaptive histogram equalization and its variations," Comput. Vision graphics and Image Proc. 39, 355-368, 1987 and the citations thereof. AHE again tends to work well when the aesthetic appearance of the image is not critical, but the information content of the image (that is, i.e. how well details are visible) is critical. When these goals and assumptions are not in place, histogram flattening and its known modifications work poorly.
Also noted is R. C. Gonzalez and P. Wintz, "Image Enhancement by Histogram Modification Techniques", Digital Image Processing, Addison-Wesley Publishing, 1977, p. 118 et seq., describing histogram flattening functions known in the art.
U.S. patent application Ser. No. 08/133,231 entitled "Image-Dependent Luminance Enhancement" filed Oct. 7, 1993 by R. Eschbach et. al., teaches a method of improving the contrast in a natural scene image, in which the image is converted from an original set of color coordinates to an expression where one term has a relationship to overall image intensity or density. A global histogram of the image is derived for that term, which plots the populations of pixels at each possible level of density in the image. That histogram is operated on with a filter having the characteristic of weakening strong peaks and valleys in the function, but not effecting flat portions of the histogram. The filtered histogram signal is used for controlling the TRC mapping in a device at which the image is to be output. Contrast is estimated in areas likely to have the most image information, using the method described above, and further, dividing the image into a number of segments, each describable by a local histogram signal for that image segment. Each local histogram signal is compared to the global histogram, to determine local image variations. From the comparison of the local histograms with the global histogram, a relevant histogram signal is derived and directed to the histogram filter in its place. The TRC derived from the relevant image histogram is applied to the color channels of the image.
A problem arises in the circumstance where the thusly determined region of interest does not provide sufficient information needed to improve image appearance.
The references cited are herein incorporated by reference.